Muse Glimmer 30B vs Gemma 4 31B

Analysis

Muse Glimmer 30B and Gemma 4 31B are close to the same size (27.8B and 30.7B parameters) and launched about four months apart in 2026, but the more interesting overlap is architectural: both use a sliding-window local-to-global attention pattern to keep their KV cache from growing without bound, the same family of technique, not just a similar result. What makes this comparison worth a closer look is that the two implementations of that idea land in very different places: Gemma 4 31B's window is smaller than Muse Glimmer's, yet its KV cache at long context ends up more than six times larger.

Muse Glimmer 30B repeats a block of three sliding-window layers per one full-attention layer across its 52 layers, capping 39 of them to a 2,048-token window. Gemma 4 31B repeats five sliding-window layers per one full-attention layer across 60 layers, capping 50 of them to a smaller 1,024-token window, per google/gemma-4-31b-it's published config, the same local-to-global pattern Gemma 3 introduced. A smaller window and a higher proportion of capped layers would suggest Gemma should end up with the lighter KV cache. It doesn't. At 131,072 tokens, the context length where Muse Glimmer's own window ends (Gemma continues on to 262,144), Muse Glimmer's KV cache is 1.8 GB against Gemma's 11.6 GB. The reason is layer width, not layer count: Gemma's 10 full-attention layers each carry a 2,048-wide KV entry (4 KV heads at 512 head dim), while Muse Glimmer's 13 full-attention layers each carry only 256 (2 KV heads at 128 head dim), an eight-fold difference per layer that outweighs Gemma having fewer uncapped layers overall. A tighter window alone doesn't guarantee a smaller cache; how wide each layer's cache entry is matters just as much. On capability, Meta's own comparison table (broken out below) puts Muse Glimmer ahead of Gemma 4 31B on nearly every agentic and coding measure it publishes, and by wider margins than the same benchmarks show against Qwen 3.6 27B. Muse Glimmer's own reported SWE-bench Pro score, 51.2, and Terminal-Bench 2.1 score, 51.7, both sit well clear of what Meta measured for Gemma on the same two suites. The one clear exception is GPQA Diamond, where Meta measured Gemma 4 31B at 85.7 against Muse Glimmer's 83.5, though Google's own self-reported GPQA Diamond score for Gemma 4 31B is lower, 84.3, under Google's own evaluation harness rather than Meta's. Independent reporting on Meta's release also credits Gemma 4 31B with the stronger guardrail record of the two: the lowest violation rate and attack-success rate in Meta's own safety comparison, a real advantage for deployments where resisting prompt injection matters more than raw capability. On hardware, the gap in the KV cache carries straight through to total VRAM: at a typical 8,192-token context, Muse Glimmer needs about 19.2 GB at Q4_K_M against Gemma's 22.6 GB, and at the shared 131,072-token ceiling that gap widens to 21.0 GB against 33.9 GB, past what a single 24 GB card can hold. Both models are text-and-vision-only (neither handles video) and ship under the same unrestricted Apache 2.0 license.

Bottom line: On raw capability and on VRAM, Muse Glimmer 30B is the stronger local-agent pick against Gemma 4 31B in most head-to-head categories Meta measured, and it stays comfortably inside a single 24 GB card across the entire context range these two models share, where Gemma 4 31B does not. Gemma 4 31B's case is narrower: choose it if you specifically need PhD-level science QA performance, where Meta's own comparison shows it slightly ahead on GPQA Diamond, if guardrail robustness against adversarial or injected instructions matters more to your deployment than raw agentic benchmark scores, or if you're already standardized on Google's Gemma tooling and ecosystem.